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The international journal of biostatistics
2010;6 (2): Article 3.

Targeted maximum likelihood based causal inference: Part II.

Mark J van der Laan
Author Information
  1. Mark J van der Laan: University of California - Berkeley, CA, USA.
PMID: 21731531 DOI: 10.2202/1557-4679.1241

Abstract

In this article, we provide a template for the practical implementation of the targeted maximum likelihood estimator for analyzing causal effects of multiple time point interventions, for which the methodology was developed and presented in Part I. In addition, the application of this template is demonstrated in two important estimation problems: estimation of the effect of individualized treatment rules based on marginal structural models for treatment rules, and the effect of a baseline treatment on survival in a randomized clinical trial in which the time till event is subject to right censoring.

Keywords

causal effect causal graph censored data collaborative double robust cross-validation double robust dynamic treatment regimens efficient influence curve estimating function estimator selection locally efficient loss function marginal structural models for dynamic treatments maximum likelihood estimation model selection path-wise derivative randomized controlled trials sieve super-learning targeted maximum likelihood estimation

References

  1. J Natl Cancer Inst. 2007 Nov 7;99(21):1577-82 [PMID: 17971533]
  2. Stat Med. 2008 Oct 15;27(23):4678-721 [PMID: 18646286]
  3. Int J Biostat. 2007;3(1):Article 3 [PMID: 19122793]
  4. Int J Biostat. 2010 May 17;6(1):Article 17 [PMID: 20628637]

MeSH Term

Algorithms
Anti-Retroviral Agents
CD4 Lymphocyte Count
Causality
Computer Simulation
HIV Infections
Longitudinal Studies
Models, Statistical
Probability
Randomized Controlled Trials as Topic
Research Design
Survival Analysis
Time Factors
Treatment Outcome

Chemicals

Anti-Retroviral Agents
Journal Article

Word Cloud

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